论文标题
对计算肿瘤病理学的机器学习方法，挑战和前景的综述
A review of machine learning approaches, challenges and prospects for computational tumor pathology
论文作者
论文摘要
计算病理学是精确肿瘤医学的一部分。高通量数据的整合包括基因组学，转录组学，蛋白质组学，代谢组学，病原体和放射组学与临床实践改善癌症治疗计划，治疗周期和治疗率，并帮助医生为患者预后开辟创新方法。在过去的十年中，人工智能，芯片设计和制造以及移动计算的快速进步促进了计算病理学的研究，并有可能为全坡度图像，多摩管数据和临床信息提供更好的整合解决方案。然而，肿瘤计算病理现在在数据集成，硬件处理，网络共享带宽和机器学习技术方面对肿瘤筛查，诊断和预后的应用带来了一些挑战。这篇综述从病理和技术观点，基于机器学习的方法以及计算病理学在乳腺，结肠，前列腺，肺和各种肿瘤疾病情景中的计算病理学的应用研究了计算病理学中的图像预处理方法。最后，讨论了计算病理应用中机器学习的挑战和前景。
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics. However, tumor computational pathology now brings some challenges to the application of tumour screening, diagnosis and prognosis in terms of data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology in breast, colon, prostate, lung, and various tumour disease scenarios. Finally, the challenges and prospects of machine learning in computational pathology applications are discussed.
